Cloud Object Storage for Data Lakes (2023)

Avoid on-premises equipment cost and complexity with a cloud data lake

Cloud Object Storage for Data Lakes (1)

The Growing Global Data Field

Cloud Object Storage for Data Lakes (2)

Data growth is exploding. A growing number of mobile devices, smart sensors, and smart endpoints are generating ever-increasing variety, volume, and velocity of data. IDC predicts that with the proliferation of connected devices and intelligent systems, the amount of data generated globally every year will grow from 33 ZB in 2018 to 175 ZB in 2025. (1 ZB = 1 trillion GB)

By turning vast amounts of raw data into meaningful and actionable insights, companies can accelerate the pace of business, improve employee productivity and streamline operations. Companies can optimize business processes and fine-tune sales, marketing and advertising campaigns. Municipalities and utilities can enhance public safety and services, optimize transportation and energy systems, and reduce expenses and waste. Researchers and scientists can improve our understanding of the universe, accelerate the treatment of diseases, and improve weather forecasting and climate modeling.

Big data has the potential to fundamentally change entire industries. But antiquated and expensive data storage solutions have hindered this process. In fact, most organizations cannot maintain large data sets over the long term using traditional on-premises storage solutions or first-generation cloud storage services from AWS, Microsoft Azure, or Google Cloud Platform. In fact, most enterprises store only the essential data needed to support primary business applications and regulatory requirements. Historical data containing valuable insights into customer behavior and market trends is often discarded.

But all that is about to change. A new generation of cloud storage has arrived, bringing utility pricing and simplicity. With Cloud Storage 2.0, you can cost-effectively store any type of data, for any purpose, for any length of time, in Wasabi's hot cloud storage. You no longer have to make painful decisions about what data to collect, where to store it, and how long to keep it.

This next-generation cloud storage is ideal for building data lakes -- large repositories where you can collect vast amounts of raw data for any purpose. In a TDWI survey of more than 250 data management professionals, nearly half of respondents said they already had a data lake in production (23%) or planned to have one within 12 months (24%) Data lake in production.


What is a data lake?

A data lake is an enterprise-wide system for securely storing different forms of data in a native format. Data lakes include a variety of data not found in traditional structured data stores (such as sensor data, clickstream data, social media data, location data, log data from servers and network devices) as well as traditional structured and semi-structured data. structured data. A data lake breaks down traditional enterprise information silos by bringing all of an enterprise's data into a single repository for analysis, without the historical constraints and hassles of schema or data transformation.

Cloud Object Storage for Data Lakes (3)

Data lakes provide the foundation for advanced analytics, machine learning, and new data-driven business practices. Data scientists, business analysts, and technical professionals can run analytics in-place using commercial or open source data analysis, visualization, and business intelligence tools of their choice. Dozens of vendors offer standards-based tools, from self-service data exploration tools for non-technical business users to advanced data mining platforms for data scientists, that help businesses monetize data lake investments and turn raw data into business value .

The diagram below depicts a data lake in an IoT implementation. Edge computing devices process and analyze local data before sending it to the data lake. For example, an edge server might perform real-time analytics, execute local business logic, and filter out data that has no inherent historical or global value.

Cloud Object Storage for Data Lakes (4)

(Video) Data Lakes in the Cloud

Data Warehouse vs. Data Mart vs. Data Lake

The terms data lake and data warehouse are often confused and sometimes used interchangeably. In fact, while both are used to store massive data sets, data lakes and data warehouses are different (and can complement each other).

A data lake is a huge pool of data that can contain any type of data -- structured, semi-structured or unstructured.

A data warehouse is a repository of structured, filtered data that has been processed for a specific purpose. In other words, a data warehouse is well organized and contains well-defined data.

Data marts are a subset of data warehouses that are used by specific enterprise business units for specific purposes, such as supply chain management applications.

James Dixon, the originator of the data lake term, explains these differences by way of analogy: "If you think of a data mart as a store for bottled water—cleaned, packaged, and structured for consumption— — A data lake is a gigantic body of water in its more natural state. The contents of a data lake are poured from the source to fill the lake, and various users of the lake can come and inspect, dive into, or sample.”

A data lake can be used in conjunction with a data warehouse. For example, you can use a data lake as a landing and staging repository for a data warehouse. You can use a data lake to organize or clean data before entering it into a data warehouse or other data structure.

Unmanaged data lakes risk becoming data swamps, with no governance or quality decisions applied to the data, radically reducing the value of the collected data by "mixing" together mixed-quality data in ways that are difficult to rely on Effectiveness of decisions made by data.

The diagram below depicts a typical data lake technology stack. A data lake includes scalable storage and computing resources; data processing tools for managing data; analysis and reporting tools for data scientists, business users, and technical staff; and general data governance, security, and operations systems.

Cloud Object Storage for Data Lakes (5)

You can implement a data lake in an enterprise data center or in the cloud. Many early adopters deployed data lakes on-premises. As data lakes become more pervasive, many mainstream adopters are looking to cloud-based data lakes to accelerate time to value, lower TCO, and increase business agility.

On-premises data lakes are CAPEX and OPEX intensive

You can implement a data lake in an enterprise data center using commodity servers and local (internal) storage. Today, most on-premises data lakes use commercial or open source versions ofHadoop, a popular high-performance computing framework, as a data platform. (insideTDWI Survey, 53% of respondents use Hadoop as their data platform, while only 6% use a relational database management system. )

You can combine hundreds or thousands of servers to create scalable and elastic Hadoop clusters capable of storing and processing massive data sets. The diagram below depicts the technology stack for an on-premises data lake on Apache Hadoop.

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Technology stack includes:

Hadoop MapReduce
A software framework for easily writing applications that process large amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.
learn more

Hadoop Yarn
A framework for job scheduling and cluster resource management.
learn more

Hadoop Distributed File System (HDFS)
A high-performance file system designed to run on low-cost servers with cheap internal disk drives.
learn more

(Video) Building Data Lake Platform with Oracle Object Storage

On-premises data lakes offer high performance and strong security, but they are notoriously expensive and complex to deploy, manage, maintain and scale. Disadvantages of on-premises data lakes include:

lengthy installation
Building your own data lake takes a lot of time, effort, and money. You must design and build systems; define and establish security and management systems and best practices; procure, build and test computing, storage and networking infrastructure; identify, install and configure all software components. It typically takes months (often more than a year) to get an on-premises data lake up and running in production.

high capital expenditure
Large upfront equipment expenditures can lead to unbalanced business models with low ROI and long payback periods. Servers, disks, and networking infrastructure are all over-engineered to meet peak traffic demands and future capacity needs, so you're always paying for idle computing resources and unused storage and networking capacity.

high operating costs
Recurring costs for power, cooling, and rack space; monthly hardware maintenance and software support costs; and ongoing hardware management costs all lead to high equipment operating expenses.

high risk
Ensuring business continuity (replicating live data to a secondary data center) is an expensive proposition out of the reach of most businesses. Many businesses back up data to tape or disk. In the event of a disaster, rebuilding systems and restoring operations can take days or even weeks.

Complex System Management
Running an on-premises data lake is a resource-intensive proposition that diverts valuable (and expensive) IT staff from more strategic work.

Cloud Data Lakes Eliminate Equipment Cost and Complexity

You can implement a data lake in the public cloud to avoid equipment expense and hassle and accelerate big data initiatives. General benefits of cloud-based data lakes include:

Quick time to value
You can reduce rollout time from months to weeks by eliminating infrastructure design work and hardware acquisition, installation, and startup tasks.

no capex
You can avoid upfront capital expenditures, better align spending with business needs, and free up capital budgets for other projects.

No equipment operating costs
You can eliminate ongoing equipment operating expenses (power, cooling, real estate), annual hardware maintenance expenses, and recurring system administration expenses.

Instant and unlimited scalability
You can add compute and storage capacity on demand to meet rapidly changing business needs and improve customer satisfaction (rapid response to line-of-business needs).

independent scaling
Unlike on-premises Hadoop implementations that rely on servers with internal storage, cloud implementations allow you to independently scale compute and storage capacity to optimize costs and maximize resource utilization.

reduce risk
You can replicate data across regions to increase resiliency and ensure continuous availability in the event of a disaster.

Simplify operation
You can free up your IT staff to focus on the strategic tasks of supporting your business (with the cloud provider managing the physical infrastructure).

First-gen cloud storage services are too expensive and complex for data lakes

Cloud-based data lakes are much easier and less expensive to deploy, scale, and operate than on-premises data lakes. That said, first-generation cloud object storage services such as AWS S3, Microsoft Azure Blob Storage, and Google Cloud Platform Storage were inherently expensive (in many cases as expensive as on-premises storage solutions) and complex. Many businesses are looking for simpler, more affordable storage services for their data lake initiatives. Limitations of first-generation cloud object storage services include:

Expensive and confusing service tiers
Traditional cloud providers sell several different types (tiers) of storage services. Each tier serves a different purpose, such as primary storage for active data, active archive storage for disaster recovery, or inactive archive storage for long-term data retention. Each has unique performance and resiliency characteristics, SLAs, and pricing tables. Complex fee structures with multiple pricing variables make it difficult to make informed choices, forecast costs and manage budgets.

supplier lock-in
Each service provider supports a unique API. Switching services is an expensive and time-consuming proposition—you must rewrite or replace existing storage management tools and applications. To make matters worse, traditional providers charge exorbitant data transfer (egress) fees to move data out of their clouds, making it costly to switch providers or leverage a mix of providers.

(Video) Cloud Object Storage: Do more with your data on IBM Cloud.

Beware of Tiered Storage Services

The first generation of cloud storage providers offered confusing tiered storage services. Each storage tier is used for a specific type of data and has different performance characteristics, SLAs, and pricing plans (with complex fee structures).

While each vendor has a slightly different product mix, these tiered services are typically optimized for three different data classes.

activity data
real-time data that can be easily accessed by the operating system,
application or user. Active data is accessed frequently and requires strict read and write performance.

event archive
Occasionally accessed data is immediately available online
(Not recovered and rehydrated from offline or remote sources). Examples include backup data for quick disaster recovery or large video files that may be accessed from time to time for short periods of time.

inactive archive
Infrequently accessed data. Examples include data maintained by
Long-term compliance. Historically, inactive data was archived to tape and stored offsite.

Determining the best class of storage (and the best value) for a particular application can be a real challenge for traditional cloud storage providers. For example, Microsoft Azure offers four different object storage options: General v1, General v2, Blob Storage, and Premium Blob Storage. Each option has unique pricing and performance characteristics. Some (but not all) options support three different storage tiers with different SLAs and fees: hot storage (for frequently accessed data), cold storage (for infrequently accessed data), and archive storage (for rarely accessed data). With so many options and pricing variables, it can be nearly impossible to make an informed decision and budget accurately.

At Wasabi, we believe cloud storage should be simple. Unlike traditional cloud storage services with confusing storage tiers and complex pricing schemes, we offer a single offering - with predictable, affordable and straightforward pricing - that can meet any cloud storage need. You can use Wasabi with any data storage class: active data, active archive, and inactive archive.

Wasabi Hot Cloud Storage for Data Lakes

Wasabi Hot Cloud Storage is an extremely affordable, fast and reliable cloud object storage for any purpose. Unlike first-generation cloud storage services with confusing storage tiers and complicated pricing schemes, Wasabi is easy to understand and extremely cost-effective to scale. Wasabi is great for storing large amounts of raw data.

Key benefits of Wasabi for data lakes include:

commodity pricing
Wasabi Hot Cloud Storage costs $0.0059/GB/month. Compared to $.023/GB/month for Amazon S3 Standard, $.026/GB/month for Google Multi-Regional, and $.046/GB/month for Azure RA-GRS Hot.

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Unlike AWS, Microsoft Azure, and Google Cloud Platform, we do not charge additional fees to retrieve data from storage (egress fees). We do not charge additional fees for PUT, GET, DELETE or other API calls.

Read our briefing on new economic technologies for cloud storage

outstanding performance
Wasabi's parallel system architecture provides faster read/write performance than first-generation cloud storage services, with significantly faster first-byte speed.

Download our performance benchmark report Wasabi Feature
Strong Data Persistence and Protection
Wasabi hot cloud storage is designed to provide extremely high data durability, integrity and security. Optional data immutability features protect against accidental deletions and administrative errors; protect against malware, errors, and viruses; and improve compliance.

Read our brief on the powerful security technology Wasabi Feature

Read our technical brief on data immutability Wasabi Feature

(Video) Azure Data Lake Storage Gen 2 Overview

Wasabi Hot Cloud Storage for Apache Hadoop Data Lakes

If you are running your data lake on Apache Hadoop, you can use Wasabi hot cloud storage as an economical alternative to HDFS, as shown in the diagram below. Wasabi hot cloud storage is fully compatible with AWS S3 API. You can use the Hadoop Amazon S3A connector (part of the open source Apache Hadoop distribution) to integrate S3 and S3-compatible storage clouds such as Wasabi into various MapReduce streams.

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You can use Wasabi hot cloud storage as part of a multi-cloud data lake implementation to improve choice and avoid vendor lock-in. A multi-cloud approach lets you independently scale data lake compute and storage resources using best-of-breed providers.

Wasabi works through partnerships with leading colocation, carrier hotel and exchange providers such asEquinix,flexibleandspotlight network.These dedicated network connections avoid Internet delays and bottlenecks, providing fast and predictable performance. you can alsoConnect to your private cloudStraight to the mustard. Unlike first-generation cloud storage providers,WasabiYou never pay for data transfer (egress). In other words, you are free to move data out of Wasabi.

Economical business continuity and disaster recovery

Wasabi is hosted in multiple geographically distributed data centers for resiliency and high availability. You can replicate data across Wasabi regions for business continuity, disaster recovery, and data protection as follows.

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For example, you can replicate data across three different Wasabi data centers (regions) using the following method:

  • Wasabi data center 1 is used for active data storage (primary storage).
  • Wasabi Datacenter 2 acts as an active archive for backup and recovery (a hot backup in case Datacenter 1 becomes inaccessible).
  • Wasabi Data Center 3 ASChangelessData storage (protecting data from mismanagement, accidental deletion, and ransomware). Immutable data objects cannot be deleted or modified by anyone, including Wasabi.

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(Video) Cloud Data Lake: How to Leverage the Future for Data Storage and Analysis // Subsurface Summer 2020


Cloud Object Storage for Data Lakes? ›

Amazon S3 is the best place to build data lakes because of its unmatched durability, availability, scalability, security, compliance, and audit capabilities.

Which storage is best for data lake? ›

Amazon S3 is the best place to build data lakes because of its unmatched durability, availability, scalability, security, compliance, and audit capabilities.

Do data lakes use object storage? ›

A data lake is a central location that holds a large amount of data in its native, raw format. Compared to a hierarchical data warehouse, which stores data in files or folders, a data lake uses a flat architecture and object storage to store the data.

What is object storage in data lake? ›

Object storage, also known as object-based storage, is a computer data storage architecture designed to handle large amounts of unstructured data. Unlike other architectures, it designates data as distinct units, bundled with metadata and a unique identifier that can be used to locate and access each data unit.

Is cloud storage a data lake? ›

Stay organized with collections Save and categorize content based on your preferences. This article discusses how you might use a data lake on Google Cloud. A data lake offers organizations like yours the flexibility to capture every aspect of your business operations in data form.

What is the difference between Blob storage and data lake? ›

In summary, Azure Data Lake Storage Gen2 is ideal for big data analytics workloads, while Blob storage is ideal for storing and accessing unstructured data. Both solutions offer strong security features and are cost-effective compared to traditional data storage solutions.

Is data lake a Blob storage? ›

For example, Data Lake Storage Gen2 provides file system semantics, file-level security, and scale. Because these capabilities are built on Blob storage, you also get low-cost, tiered storage, with high availability/disaster recovery capabilities.

Is Snowflake a data lake? ›

Snowflake Has Always Been a Hybrid of Data Warehouse and Data Lake.

Is S3 a data lake? ›

Amazon S3 provides an optimal foundation for a data lake because of its virtually unlimited scalability and high durability. You can seamlessly and non-disruptively increase storage from gigabytes to petabytes of content, paying only for what you use.

Is S3 object storage or file storage? ›

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance.

Does AWS have data lakes? ›

To support our customers as they build data lakes, AWS offers Data Lake on AWS, which deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets.

Is Azure a data lake or data warehouse? ›

Azure Data Lake Storage is a massively scalable and secure Data Lake for high-performance analytics workloads. Azure Lake Data Storage was formerly known and is sometimes still referred to as the Azure Data Lake Store.

Does Azure have a data lake? ›

Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages.

What type of storage is data lake? ›

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Learn more about modernizing your data lake on Google Cloud.

What are the types of data storage data lake? ›

A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video).

What is the difference between Databricks and data lake? ›

Azure Databricks is a managed service for interactive analytics on large datasets. Azure Data Lake is a service that allows organizations to store and process large amounts of data, while Azure Databricks is a cloud-hosted big data environment.

What is Snowflake vs data lake? ›

The biggest difference between Snowflake and a data lakehouse platform is that Snowflake's hybrid model has better capabilities for the security and governance of sensitive data, as well as more automation, better economics, and better performance.

What is the difference between Snowflake and Azure Data Lake? ›

Snowflake offers native connectivity to multiple BI, data integration, and analytics tools . Azure comes with integration tools such as logic apps, API Management, Service Bus, and Event Grid for connecting to third-party services.

What are the three types of blob storage? ›

The storage service offers three types of blobs, block blobs, append blobs, and page blobs.

What is Blob storage vs object storage? ›

Blob storage is a type of object storage. Object storage keeps files or blobs in a flat "data lake" or "pool" with no hierarchy; a data lake/pool is a large collection of unstructured data.

Does Snowflake use Blob storage? ›

Snowflake currently supports loading from blob storage only. Snowflake supports the following types of storage accounts: Blob storage.

What is data reservoir vs data lake? ›

Data warehouses are often used for daily and operational business decisions and processes, whereas lakes are used to harness big amounts of data and benefit from the raw data. Data lakes are often used as a part of machine learning or advanced analytics solutions.

Is Azure Databricks a data lake? ›

Azure Databricks is a fully managed first-party service that enables an open data lakehouse in Azure.

Can Snowflake replace data lake? ›

Snowflake's platform provides both the benefits of data lakes and the advantages of data warehousing and cloud storage. With Snowflake as your central data repository, your business gains best-in-class performance, relational querying, security, and governance.

Is BigQuery a data lake or data warehouse? ›

For marketing departments, the best solution for storing data is a data lake — specifically, the popular and convenient Google BigQuery.

What is AWS equivalent of Azure Data Lake? ›

Azure Data Lake is the competitor to AWS Cloud Formation. As with AWS, Azure Data Lake is centered around its storage capacity, with Azure blob storage being the equivalent to Amazon S3 storage.

Does data lake use ETL? ›

ETL is what happens within a Data Warehouse and ELT within a Data Lake. ETL is the most common method used when transferring data from a source system to a Data Warehouse.

Is ETL a data lake? ›

The original ETL processes were developed under the data warehouse model, in which data was structured and organized systematically. But some ETL technologies have adapted to the emergence of data lakes and are now called ELT. That's right.

Is blob storage same as S3? ›

Blob storage serves the same purpose as both AWS S3 and EBS. Table storage stores structured datasets. Table storage is a NoSQL key-attribute data store that allows for rapid development and fast access to large quantities of data. Similar to AWS' SimpleDB and DynamoDB services.

What is blob storage vs S3? ›

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. For its part, Azure Blob Storage is Microsoft's object storage solution for the cloud. Blob storage is optimized for storing large amounts of unstructured data.

What is the difference between S3 and NFS? ›

From developer's perspective, NFS support is brought in by operating system and does not require much effort. On the other hand, S3 client support is not included by default in the operating system. S3 support requires special library, code changes, and integration effort to manage dependency and library version.

Is redshift a data warehouse or data lake? ›

Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze data using standard SQL and existing Business Intelligence (BI) tools. To get information from unstructured data that would not fit in a data warehouse, you can build a data lake.

How do I create a data lake in AWS S3? ›

In this walkthrough, I show you how to build and use a data lake:
  1. Create a data lake administrator.
  2. Register an Amazon S3 path.
  3. Create a database.
  4. Grant permissions.
  5. Crawl the data with AWS Glue to create the metadata and table.
  6. Grant access to the table data.
  7. Query the data using Amazon Athena.
Aug 12, 2019

Is Databricks a data warehouse or data lake? ›

The Databricks Lakehouse Platform uses Delta Lake to give you: World record data warehouse performance at data lake economics. Serverless SQL compute that removes the need for infrastructure management.

What is Delta Lake vs data lake? ›

What Is Delta Lake? Delta Lake is an open-source storage layer built atop a data lake that confers reliability and ACID (Atomicity, Consistency, Isolation, and Durability) transactions. It enables a continuous and simplified data architecture for organizations.

Is Snowflake a data warehouse? ›

Snowflake: A different data warehouse architecture

Designed with a patented new architecture to handle all aspects of data and analytics, it combines high performance, high concurrency, simplicity, and affordability at levels not possible with other data warehouses.

What is Azure data lake called? ›

Azure Data Lake Analytics is an on-demand analytics platform for big data. Users can develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, and . NET over petabytes of data. (U-SQL is a big data query language created by Microsoft for the Azure Data Lake Analytics service.)

Is Azure Data Lake PaaS or SaaS? ›

Azure Data Lake is a cloud-based PaaS solution for huge data storage. Trillions of files up to a petabyte in size can be supported. Hundreds of gigabytes of throughput azure storage blob technology with hierarchical namespace access using the Hadoop distributed file system (HDFS).

How do I create a data lake in Azure? ›

Create a Data Lake Analytics account
  1. Sign on to the Azure portal.
  2. Select Create a resource, and in the search at the top of the page enter Data Lake Analytics.
  3. Select values for the following items: ...
  4. Optionally, select a pricing tier for your Data Lake Analytics account.
  5. Select Create.
Jan 13, 2023

What is the most effective data storage? ›

SSD and Flash Drive

Flash storage uses flash memory chips to store data. Similarly, SSDs store data using flash memory. It facilitates a swift and fast data transfer between devices. The SSD and flash drives are less prone to data loss due to physical damage due to absence of moving parts.

Which data format is best for data lake? ›

Text Files – Information will often come into the data lake in the form of delimited text, JSON, or other similar formats. As discussed above, text formats are seldom the best choice for analysis, so you should generally convert to a compressed format like ORC or Parquet.

What storage does Azure Data Lake use? ›

Azure Data Lake is built on Azure Blob storage, the Microsoft object storage solution for the cloud.

Is data lake in AWS? ›

To support our customers as they build data lakes, AWS offers Data Lake on AWS, which deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets.

What are the 3 types of data storage? ›

Data can be recorded and stored in three main forms: file storage, block storage and object storage.

What is the safest form of data storage? ›

The two main types of safe data storage online solutions are direct attached storage (DAS) and network attached storage (NAS). Direct attached storage includes data storage types that are connected directly to your computer.

What is the best mode of storage of extremely large data? ›

Warehouse and cloud storage are two of the most popular options for storing big data. Warehouse storage is typically done on-site, while cloud storage involves storing your data offsite in a secure location.

How do you secure data in data lake? ›

Data Lake Security
  1. Data Lake Encryption. End-to-end encryption needs to be a default setting for cloud data lake security, with security processes such as customer-managed keys. ...
  2. Encryption Keys. ...
  3. Security Updates and Logging. ...
  4. Access Control. ...
  5. Compliance and Attestations. ...
  6. Data Isolation.

What is the difference between data lake and data reservoir? ›

data lake: Some businesses use the concept of a data reservoir to distinguish between a totally unrefined data lake and a data reservoir that's been partly filtered, secured, and made ready for consumption. Imagine a lake that's been drained and filtered into a reservoir, creating a source of potable drinking water.

What makes a successful data lake? ›

What makes a good data lake? To deliver value to both technical and business teams, a data lake needs to serve as a centralized repository for both structured and unstructured data, while allowing data consumers to pull data from relevant sources to support various analytic use cases.

What is the difference between blob storage and data lake Gen2? ›

Azure Blob Storage is a flat namespace storage where the users were able to create virtual directories, while Azure Data Lake Storage Gen2 has the hierarchical namespace functionality within its product.


1. Creating a Cloud Data Lake for a Trillion Dollar Organization | How to Build a Cloud Data Lake
2. Data Lake: Getting To Your Cloud Data
(Advoco, Part of Accenture)
3. Azure Storage: Blob vs Data Lake
4. Digging and Filling Data Lakes (Cloud Next '18)
(Google Cloud Tech)
5. Azure Data Lake Design and Implementation Patterns
6. Azure Data Lake Storage (Gen 2) Tutorial | Best storage solution for big data analytics in Azure
(Adam Marczak - Azure for Everyone)
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